V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map (1711.07399v3)

Abstract: Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly non-linear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time. Our system outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge. The code is available in https://github.com/mks0601/V2V-PoseNet_RELEASE.

• The paper introduces a novel voxel-to-voxel prediction network that transforms depth maps into precise 3D hand and human pose estimations.
• It leverages 3D CNNs with an hourglass design to predict per-voxel likelihoods, effectively addressing perspective distortion and non-linear mapping challenges.
• Experimental results on datasets like ICVL, NYU, and ITOP validate its state-of-the-art performance and promise for real-time applications.

V2V-PoseNet: Voxel-to-Voxel Prediction Network for 3D Pose Estimation

The paper "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map" introduces an innovative approach to 3D pose estimation using a voxel-based network. This method addresses critical issues found in previous models that directly map 2D depth images to 3D coordinates using conventional 2D CNNs.

Challenges in Previous Approaches

Traditional methods often grapple with two significant challenges: perspective distortion and the inherently non-linear mapping from 2D to 3D spaces. Handling depth maps as mere 2D images results in distorted representations of 3D objects, complicating the network's task of inferring accurate poses under distortion-invariant conditions. Furthermore, directly regressing 3D coordinates from these 2D projections demands solving a highly non-linear problem, presenting additional learning difficulties.

V2V-PoseNet Proposal

V2V-PoseNet innovatively reframes the 3D pose estimation problem by utilizing a voxel-to-voxel prediction format. This model processes a 3D voxelized grid rather than a 2D depth map, transforming each 3D space point into discrete voxels. The network then predicts per-voxel likelihoods for keypoints instead of regressing the coordinates directly. Such a configuration allows the model to bypass the need for distortion correction and improve the learning process's tractability.

Methodology

The V2V-PoseNet architecture leverages 3D CNNs to perform this voxel-to-voxel mapping. The model's design draws inspiration from the hourglass network, well-suited for pose estimation tasks, and includes volumetric layers to manage the additional dimension effectively. Furthermore, the network's design includes a reference point refining step to ensure accurate initial location, crucial for framing the target object appropriately.

Experimental Validation

The effectiveness of the voxel-to-voxel approach is supported by strong experimental results. V2V-PoseNet achieves state-of-the-art performance across multiple datasets, such as ICVL, NYU, and MSRA for hand pose estimation, and the ITOP dataset for human pose estimation. The model's performance is underscored by its success in the HANDS 2017 3D hand pose estimation challenge, where it outperformed competing methods.

Results and Implications

Empirical results show significant improvements in average 3D distance error, with the voxel-to-voxel method yielding better accuracies compared to mapping 2D inputs to 3D coordinates directly. These advancements suggest a promising direction for future development in both model performance and application scope, including real-time applications given its efficient runtime.

Future Directions

The implications of this research are substantial for both practical implementations and theoretical advancements in AI and computer vision. Future work may explore further optimizations in network architecture, enhancements in real-time processing capabilities, and application to broader domains beyond hand and human pose estimation.

In conclusion, V2V-PoseNet exemplifies a noteworthy improvement in 3D pose estimation, leveraging the power of volumetric data representation and advancing the understanding of depth-based vision tasks. The model serves as a significant milestone, prompting further exploration into voxel-based processing techniques in machine learning.